Browsing by Author "Tzeng, GH"

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A fuzzy data mining algorithm for finding sequential patterns
(2003-04-01)
Hu, YC
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Chen, RS
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Tzeng, GH
;
Shieh, JH
;
科技管理研究所
Since fuzzy knowledge representation can facilitate interaction between an expert system and its users, the effective construction of a fuzzy knowledge base is important. Fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, and can thus be helpful in building a prototype fuzzy knowledge base. We define that a fuzzy sequence is an ordered fist of frequent fuzzy grids, and the length of a fuzzy sequence is the number of frequent fuzzy grids in the frequent fuzzy sequence. Frequent fuzzy grids and frequent fuzzy sequences can be determined by comparing individual fuzzy supports with the user-specified minimum fuzzy support. A fuzzy sequential pattern is just a frequent fuzzy sequence, but it is not contained in any other frequent fuzzy sequence. In this paper, an effective algorithm called the Fuzzy Grids Based Sequential Patterns Mining Algorithm (FGBSPMA) is proposed to generate fuzzy sequential patterns. A numerical example is used to show an analysis of the user visit to websites, demonstrating the usefulness of the proposed algorithm.
A fuzzy multiple objective programming to dea with imprecise data
(2004-10-01)
Yu, JR
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Tzeng, YC
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Tzeng, GH
;
Yu, TY
;
Sheu, HJ
A fuzzy multiple objective programming approach to data envelopment analysis (DEA) of imprecise data is proposed in this paper. The problems involving a mixture of imprecise and exact data for all decision making units (DMUs) could be resolved and the discriminating power of imprecise DEA (IDEA) is enhanced. Although Cooper et al. have developed IDEA to overcome the issues of imprecise data, the discriminating power is not satisfactory since too many efficient DMUs are derived. Chiang and Tzeng's approach using fuzzy multiple objective programming techniques is adopted to enhance the discriminating power of IDEA. The same data set of Cooper et al. is employed to illustrate the merit of our approach.
A fuzzy seasonal ARIMA model for forecasting
(2002-03-16)
Tseng, FM
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Tzeng, GH
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科技管理研究所
;
Institute of Management of Technology
This paper proposes a fuzzy seasonal ARIMA (FSARIMA) forecasting model, which combines the advantages of the seasonal time series ARIMA (SARIMA) model and the fuzzy regression model. It is used to forecast two seasonal time series data of the total production value of the Taiwan machinery industry and the soft drink time series. The intention of this paper is,to provide business which are affected by diversified management with a new method to conduct short-term forecasting. This model includes both interval models with interval parameters and the possible distribution of future value. Based on the results of practical application, it can be shown that this model makes good forecasts and is realistic. Furthermore, this model makes it possible for decision makers to forecast the best and worst estimates based on fewer observations than the SARIMA model. (C) 2002 Elsevier Science B.V. All rights reserved.
A general piecewise necessity regression analysis based on linear programming
(1999-08-01)
Yu, JR
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Tzeng, GH
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Li, HL
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運輸與物流管理系註：原交通所+運管所
;
資訊管理與財務金融系註：原資管所+財金所
In possibilistic regression analysis proposed by Tanaka and Ishibuchi (1992), linear programming (LP) formulation of necessity analysis has no feasible solution under the enormous variation of the given data. This work proposes a general piecewise necessity regression analysis based on LP rather than a non-linear interval model that they recommended to obtain the necessity area of the given data. In addition to maintaining a linear property, the proposed method prevents the necessity analysis from having no feasible solution. The problematic univariate example and a multivariate example with respect to different number of change-points are demonstrated by the general piecewise necessity regression. The proposed method characteristic is that, according to data distribution, practitioners can specify the number and the positions of change-points. The proposed method maintains the linear interval model and the order of necessity regression function does not need to be determined. (C) 1999 Elsevier Science B.V. All rights reserved.
A multiobjective programming approach for selecting non-independent transportation investment alternatives
(1996-08-01)
Teng, JY
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Tzeng, GH
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運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
This article presents a new method for SElecting Non-independent TRansportation investment Alternatives (SENTRA). This method utilizes effective distance heuristic algorithm which attempts to maximize the achieved objectives needed to satisfy available resources. Since transportation investment planning cannot avoid dealing with issues of interdependence among alternatives, this paper will consider four types of investment alternatives: independence, complementarity, substitution and common complementary substitution. Transportation investment alternative selection problem can be formulated in terms of the 0-1 multiobjective multidimensional knapsack problem. Possessing the characteristics of NP-completeness, strict computation is not necessary for the optimal solution, but simple computation for near-optimal solution is expected. The method is proposed in this paper so as to attain the near-optimal solution, which, aside from ranking the selected transportation investment alternatives, can easily perform sensitivity analysis. Finally, an example is presented to illustrate the method. Copyright (C) 1996 Elsevier Science Ltd
A new application of fuzzy set theory to the Black-Scholes option pricing model
(2005-08-01)
Lee, CF
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Tzeng, GH
;
Wang, SY
;
科技管理研究所
;
資訊管理與財務金融系註：原資管所+財金所
The Black-Scholes Option pricing model (OPM) developed in 1973 has always been taken as the cornerstone of option pricing model. The generic applications of such a model are always restricted by its nature of not being suitable for fuzzy environment since the decision-making problems occurring in the area of option pricing are always with a feature of uncertainty. When an investor faces an option-pricing problem, the outcomes of the primary variables depend on the investor's estimation. It means that a person's deduction and thinking process uses a non-binary logic with fuzziness. Unfortunately, the traditional probabilistic B-S model does not consider fuzziness to deal with the aforementioned problems. The purpose of this study is to adopt the fuzzy decision theory and Bayes' rule as a base for measuring fuzziness in the practice of option analysis. This study also employs 'Fuzzy Decision Space' consisting of four dimensions, i.e. fuzzy state; fuzzy sample information, fuzzy action and evaluation function to describe the decision of investors, which is used to derive a fuzzy B-S OPM under fuzzy environment. Finally, this study finds that the over-estimation exists in the value of risk interest rate, the expected value of variation stock price, and in the value of the call price of in the money and at the money, but under-estimation exists in the value of the call price of out of the money without a consideration of the fuzziness. (C) 2005 Elsevier Ltd. All fights reserved.
A novel algorithm for dynamic factor analysis
(2006-04-15)
Huang, JJ
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Tzeng, GH
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Ong, CS
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科技管理研究所
;
Institute of Management of Technology
In this paper, a dynamic factor model is proposed to extract the dynamic factors from time series data. In order to deal with the problem of scaling, the cross-correlation matrices (CCM) are first employed to cluster the time series data. Then, the dynamic factors are extracted using the revised independent component analysis (ICA). In addition, a numerical study is used to demonstrate the proposed method. On the basis of the simulated results, we can conclude that the proposed method can really extract the effective dynamic factors. (c) 2005 Elsevier Inc. All rights reserved.
A novel algorithm for uncertain portfolio selection
(2006-02-01)
Huang, HJ
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Tzeng, GH
;
Ong, CS
;
運輸與物流管理系註：原交通所+運管所
;
科技管理研究所
In this paper, the conventional mean-variance method is revised to determine the optimal portfolio selection under the uncertain situation. The possibilistic area of the return rate is first derived using the possibisitic regression model. Then, the Mellin transformation is employed to obtain the mean and the risk by considering the uncertainty. Next, the revised mean-variance model is proposed to deal with the problem of uncertain portfolio selection. In addition, a numerical example is used to demonstrate the proposed method. On the basis of the numerical results, we can conclude that the proposed method can provide the more flexible and accurate results than the conventional method under the uncertain portfolio selection situation. (c) 2005 Elsevier Inc. All rights reserved.
A novel hybrid model for portfolio selection
(2005-10-15)
Ong, CS
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Huang, HJ
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Tzeng, GH
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科技管理研究所
;
Institute of Management of Technology
As we know, the performance of the mean-variance approach depends on the accurate forecast of the return rate. However, the conventional method (e.g. arithmetic mean or regression-based method) usually cannot obtain a satisfied solution especially under the small sample situation. In this paper, the proposed method which incorporates the grey and possibilistic regression models formulates the novel portfolio selection model. In order to solve the multi-objective quadric programming problem, multi-objective evolution algorithms (MOEA) is employed. A numerical example is also illustrated to show the procedures of the proposed method. On the basis of the numerical results, we can conclude that the proposed method can provide the more flexible and accurate results. (c) 2004 Elsevier Inc. All rights reserved.
A revised minimum spanning table method for optimal expansion of competence sets
(2000)
Chiang, CI
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Li, JM
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Tzeng, GH
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Yu, PL
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運輸與物流管理系註：原交通所+運管所
Competence set analysis is a new approach to solve an import aspect of challenging MCDM problems. Thus, many researchers have developed efficient methods for solving the optimal expansion of competence sets (please see the references). The minimum spanning table method (MST) proposed by Feng and Yu (1997) is an efficient algorithm using spreadsheet tableaus to solve optimal expansion problems. In order to develop a computer program, in this paper, we propose the revised algorithm of MST, which can efficiently solve the optimal expansion problems. We develop a user-friendly program called MINST, which can solve fairly large-scale expanding problems even with PC of pentium 133.
A spatial and temporal bi-criteria parallel-savings-based heuristic algorithm for solving vehicle routing problems with time windows
(1997)
Tzeng, GH
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Huang, WC
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Teodorovic, D
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
This paper proposes a spatial and temporal bi-criteria parallel-savings-based heuristic algorithm for solving vehicle-routing problems with time windows. The purpose of the algorithm is to reduce transportation costs and to satisfy the specific times, within time windows, which are required by customers. For evaluating the performance of the algorithm, two separate sets of time windows are created by generating data randomly. The test results reveal that both the preciseness and stability of the solutions perform much better than those based on the insertion method.
A weight-assessing method with habitual domains
(1998-10-16)
Tzeng, GH
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Chen, TY
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Wang, JC
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管理學院
;
College of Management
Weights of criteria are the important factors in decision-making. However, from a behavioral perspective, traditional weighting methods account for too few factors to deal with decisions properly. Based on the behavior mechanism and the theory of habitual domains, this study is undertaken to develop a new weight-assessing model that treats decisionmaking as a dynamically adjusting process proceeding from the ideal to actual states. The new model is built upon the dynamic analysis for the connectivities between criteria instead of the static analysis of traditional models. Finally, we have studied Taipei City motorcycle users, mode-choice behavior through questionnaire in order to show the applicability of the new model. From the empirical results, it is found that our weight-assessing method has significant application potential in practice. (C) 1998 Elsevier Science B.V. All rights reserved.
Acquisition of compound skills and learning costs for expanding competence sets
(2003-09-01)
Hu, YC
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Chen, RS
;
Tzeng, GH
;
Chiu, YJ
;
科技管理研究所
For each decision problem, there is a competence set consisting of ideas, knowledge, information, and skills for solving that problem. When decision makers have not acquired the competence set, it is more difficult for them to make decisions. In order to effectively acquire a needed competence set to cope with the problem they face, finding an appropriate learning sequence for acquiring needed single skills for decision makers, the so-called competence set expansion, is very necessary. A compound skill represents a collection of single skills that might be acquired, and some useful compound skills can be added to the needed competence set to help acquire some single skills. To effectively expand the competence set, effective acquisitions of compound skills and learning costs are both necessary. This paper thus proposes a data mining technique to extract potentially useful compound skills from single skills. Subsequently, an effective method is proposed to obtain the learning cost between any two skills. A computer simulation is employed to further show that it is feasible to use those potentially useful compound skills to facilitate the acquisition of single skills through a known integer programming method for expanding the competence set. (C) 2003 Elsevier Ltd. All rights reserved.
An emerging approach for strategy evaluation in fuel cell development
(2005)
Yuan, BJC
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Wang, CP
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Tzeng, GH
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科技管理研究所
;
Institute of Management of Technology
This is the first research paper on the topic of Taiwan fuel cell development to combine the two theoretical (AHP and FMCDM) approaches. The conclusion suggests that the priority for Taiwan to develop fuel cell are the fields of 3C electronics, power generation equipment, motorcycles and automobiles. The difficulties the industry has encountered and faced, in turn, are the technology bottlenecks, insufficient R&D investment, high costs, unclear government policy and short supply of R&D professionals. Experts believe the top five strategic actions needed to address the problems are to realise the clean energy policy, establish a national level research programme, increase R&D budget, carefully select the niche products and to plan the operating demonstration or pilot zone. The result will serve as a reference for the government to stipulate relevant industry policy.
An extended approach of multicriteria optimization for MODM problems
(2003)
Chiou, HK
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Tzeng, GH
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科技管理研究所
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Institute of Management of Technology
In this paper we propose an extended method for multicriteria optimization and compromise solution to solve multiple objective decision making (MODM) problems. This method assumes that optimal compromise solution should have the shortest distance from the positive ideal solution (PIS) as well as the longest distance from the negative ideal solution (NIS). We use the membership function of fuzzy set theory to express the satisfaction level, and use max-min operation for this bi-objective programming problem To illustrate this procedure, prequalification for the project bidding process of an outsourcing partner for semiconductor enterprise in Taiwan is solved by use of our procedure.
Application of multiple criteria decision making for network improvement
(1997-03-01)
Tzeng, GH
;
Tsaur, SH
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
In this paper we propose application of multiple criteria decision making to problems of a metropolitan network improvement plan. Initially, a bilevel multiple objective network design model is considered in two objectives which are minimal government budget and minimal total travel time of road users. We seek feasible improvement alternatives among those bottleneck links in an existing road network structure and travel demand. We present an effective heuristic algorithm to obtain noninferior solutions; then ELECTRE III multiple criteria decision making and group decision making are used to evaluate and to select a compromise solution among those noninferior solutions. From the design phase in multiple criteria decision making, multiple objective mathematical programming is used to formulate a continuous network design model. However, from the phase of evaluation, multiple criteria decision making to solve the discrete network design problem. The network of metropolitan Taipei is taken as an example to illustrate the operation of this model.
Applied hybrid grey model to forecast - Seasonal time series
(2001-06-01)
Tseng, FM
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Yu, HC
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Tzeng, GH
;
科技管理研究所
;
Institute of Management of Technology
The grey forecasting model has been successfully applied to finance, physical control, engineering, economics, etc. However, no seasonal time series forecast has been tested. The authors of this paper proved that GM(1,1) grey forecasting model is insufficient for forecasting time series with seasonality. This paper proposes a hybrid method that combines the GM(1,1) grey forecasting model and the ratio-to-moving-average deseasonalization method to forecast time series with seasonality characteristics. Three criteria, i.e., the mean squares error (MSE), the mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to compare the performance of the hybrid model against other four models, i.e., the seasonal time series ARIMA model (SARIMA), the neural network back-propagation model combined with grey relation, the GM(1,1) grey model with raw data, the GM(1,N) grey model combined with grey relation. The time series data of the total production value of Taiwan's machinery industry (January 1994 to December 1997) and the sales volume of soft drink reported from Montgomery's book were used as test data sets. Except for the out-of-sample error of the Taiwan machinery production value time series, the MSE, the MAE, and the MAPE of the hybrid model were the lowest. (C) 2001 Elsevier Science Inc.
Arrow decision logic
(2005)
Fan, TF
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Liu, DR
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Tzeng, GH
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科技管理研究所
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資訊管理與財務金融系註：原資管所+財金所
In this paper, we propose arrow decision logic (ADL), which combines the main features of decision logic and arrow logic. Decision logic represents and reasons about knowledge extracted from decision tables based on rough set theory, while arrow logic is the basic modal logic of arrows. The semantic models of ADL are pairwise comparison tables, which are useful in rough set-based multicriteria analysis. Consequently, ADL can represent preference knowledge induced from multicriteria decision tables.
Assessing strategies for reducing greenhouse gas emissions in urban transportation sector
(1998)
Tzeng, GH
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Weyant, JP
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交大名義發表
;
National Chiao Tung University
Preventing global warming has become the consensus of human beings. "United Nations Framework Convention on Climate Change" has assured the goal of reducing greenhouse gas emission. There are two sources of greenhouse gas emission: fixed sources and mobile sources. Mobile sources are those vehicles in motion which emit gas pollutant from energy consumption. Because of the technology limitation, reducing or recycling the gases emitted by mobile sources is rather difficult. This study proposes a model for assessing strategies of reducing the greenhouse gas emission in transportation sector by focusing on technology innovation, substitution of transportation modes (automobiles), transportation system management, adjustment of transportation related lifestyle and urban development structure. The relative importance and effect of every item and criterion are evaluated by using pairwise comparison of AHP. The assessments and rankings of strategies are made by using total weighting score method. Finally, according to the analysis of the results, the strategies are proposed to the future planning of decreasing greenhouse gas emission.
Assessing weights of product attributes from fuzzy knowledge in a dynamic environment
(2004-04-01)
Hu, YC
;
Hu, JS
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Chen, RS
;
Tzeng, GH
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科技管理研究所
Fuzzy knowledge of consumers' frequent purchase behaviors can be extracted from transaction databases. To effectively supporting decision makers, it is necessary to use fuzzy knowledge to assess weights or degrees of consumers' attentiveness to product attributes. From the standpoint of habitual domains, frequent purchase behaviors can be viewed as ideas that are contained in the reachable domain of customers. In addition, this reachable domain is changeable with time, due to the dynamic environment. This paper thus proposes a two-phase learning method with adaptive capability. The first phase builds a fuzzy knowledge base by discovering frequent purchase behaviors from transaction databases; the second phase finds weights of product attributes by a single-layer perceptron neural network. Indeed, customers are asked to evaluate alternatives and attributes through questionnaire. Then, each alternative can be transformed into a piece of input training data for the neural network by the fuzzy knowledge base and part-worths of attributes' levels. After completing the training task, we can find weights from connection weights. Simulation results demonstrate that the proposed methods can use fuzzy knowledge to effectively find customers' attentive degrees of attributes. (C) 2002 Elsevier B.V. All rights reserved.